{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:25:04Z","timestamp":1775913904954,"version":"3.50.1"},"reference-count":81,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>\n            Rapid advancement in machine learning is increasing the demand for effective graph data analysis. However, real-world graph data often exhibits class imbalance, leading to poor performance of standard machine learning models on underrepresented classes. To address this,\n            <jats:underline>\n              <jats:bold>C<\/jats:bold>\n            <\/jats:underline>\n            lass-\n            <jats:underline>\n              <jats:bold>I<\/jats:bold>\n            <\/jats:underline>\n            mbalanced\n            <jats:underline>\n              <jats:bold>L<\/jats:bold>\n            <\/jats:underline>\n            earning on\n            <jats:underline>\n              <jats:bold>G<\/jats:bold>\n            <\/jats:underline>\n            raphs (CILG) has emerged as a promising solution that combines graph representation learning and class-imbalanced learning. This survey provides a comprehensive understanding of CILG\u2019s current state-of-the-art, establishing the first systematic taxonomy of existing work and its connections to traditional imbalanced learning. We critically analyze recent advances and discuss key open problems. A continuously updated reading list of relevant articles and code implementations is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/yihongma\/CILG-Papers\">https:\/\/github.com\/yihongma\/CILG-Papers<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3718734","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T11:19:06Z","timestamp":1739963946000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Class-Imbalanced Learning on Graphs: A Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-5953","authenticated-orcid":false,"given":"Yihong","family":"Ma","sequence":"first","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2795-6080","authenticated-orcid":false,"given":"Yijun","family":"Tian","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4322-1076","authenticated-orcid":false,"given":"Nuno","family":"Moniz","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3932-5956","authenticated-orcid":false,"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","volume-title":"Proceedings of the ICNSC","author":"Bai Xiang-En","year":"2022","unstructured":"Xiang-En Bai, Jing An, Zi-Bo Yu, Han-Qiu Bao, and Ke-Fan Wang. 2022. 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